[Tests] Add qwen3-8b nightly test (#5597)

### What this PR does / why we need it?
Add qwen3-8b nightly test 

- vLLM version: v0.13.0
- vLLM main:
7157596103
---------
Signed-off-by: wxsIcey <1790571317@qq.com>

tests/e2e/nightly/single_node/models/test_qwen3_8b.py

@@ -0,0 +1,99 @@
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+from typing import Any
+
+import openai
+import pytest
+from vllm.utils.network_utils import get_open_port
+
+from tests.e2e.conftest import RemoteOpenAIServer
+from tools.vllm_bench import run_vllm_bench_case
+
+MODELS = [
+    "Qwen/Qwen3-8B",
+]
+
+prompts = [
+    "San Francisco is a",
+]
+
+api_keyword_args = {
+    "max_tokens": 10,
+}
+
+vllm_bench_cases = {
+    "dataset-name": "random",
+    "num_prompts": 1000,
+    "request_rate": 20,
+    "random_input_len": 128,
+    "max_concurrency": 40,
+    "random_output_len": 100,
+}
+
+baseline_throughput = 1622.08  # baseline throughput for Qwen3-8B
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.asyncio
+async def test_models(model: str) -> None:
+    port = get_open_port()
+    env_dict = {
+        "TASK_QUEUE_ENABLE": "1",
+        "HCCL_OP_EXPANSION_MODE": "AIV",
+        "VLLM_ASCEND_ENABLE_PREFETCH_MLP": "1",
+    }
+    server_args = [
+        "--async-scheduling",
+        "--distributed-executor-backend",
+        "mp",
+        "--tensor-parallel-size",
+        "1",
+        "--port",
+        str(port),
+        "--max-model-len",
+        "5500",
+        "--max-num-batched-tokens",
+        "40960",
+        "--compilation-config",
+        '{"cudagraph_mode": "FULL_DECODE_ONLY"}',
+        "--additional-config",
+        '{"pa_shape_list":[48,64,72,80]}',
+        "--block-size",
+        "128",
+        "--trust-remote-code",
+        "--gpu-memory-utilization",
+        "0.9",
+    ]
+
+    request_keyword_args: dict[str, Any] = {
+        **api_keyword_args,
+    }
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            env_dict=env_dict,
+                            auto_port=False) as server:
+        client = server.get_async_client()
+        batch = await client.completions.create(
+            model=model,
+            prompt=prompts,
+            **request_keyword_args,
+        )
+        choices: list[openai.types.CompletionChoice] = batch.choices
+        assert choices[0].text, "empty response"
+        # vllm bench test
+        run_vllm_bench_case(model, port, vllm_bench_cases, baseline_throughput)